Let us revisit the consequence of noisy quantum kernels as indicated in Theorem REF . Specifically, to preserve the superiority of quantum kernels carried out on NISQ machines, we can either slim the size of training datasets or suppress the effects of noise. The solution to address the first issue is apparent, i.e., the construction of a coreset [1]}, while the strategy to emphasize the second issue remains obscure. In the following, we investigate how to mitigate the negative influence of quantum noise to further enhance performance of noisy quantum kernels.
